Training Data
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Training data is the labeled or structured corpus of examples a machine learning model learns from during initial development; its composition determines which patterns the model can recognize, which biases it will reproduce, and what it will fabricate when queried about something outside its experience.
Why the source corpus matters more than the model itself
Most general-purpose large language models were trained on web-scale text: forums, articles, code repositories, and scraped documents with no particular depth in AEC. That training corpus shapes every output. A model trained without significant exposure to SF-330 structures, QBS evaluation criteria, or FAR Part 36 language will produce plausible-sounding text that is factually wrong about those things, because it is pattern-matching from adjacent domains rather than from actual federal procurement practice. The non-obvious implication: two models with identical architecture can perform radically differently on proposal tasks purely because of what each was trained on. Benchmark scores measured on general corpora tell you almost nothing about how a model will handle a scope narrative or a past performance summary.
Where training data failure shows up in a live pursuit
The failure mode is rarely obvious. A model with weak AEC training will not blank out; it will confidently populate a project approach section with language that sounds precise but misrepresents delivery methods, misnames standard contract forms, or cites evaluation criteria that do not exist in the actual solicitation. During a two-week RFP window, no one has time to fact-check every sentence the model generates, which is exactly when errors survive into final submittals. Go/no-go decisions that rely on AI-summarized opportunity data carry the same risk: if the model has no grounded training in how owners structure selection criteria for CMAR versus design-bid-build, its summary of a pre-RFP opportunity will reflect a generic construction world that may not match the actual client.
Fine-tuning and retrieval do not fully substitute for good training data
Model fine-tuning can adjust tone and improve task-specific formatting, but it does not rewrite foundational knowledge; a model fine-tuned on your past proposals still carries the gaps from its original training corpus. Retrieval-augmented generation addresses this more directly by grounding outputs in documents you provide at inference time, but it depends entirely on the quality and structure of your content library. The practical floor for any AI tool used in proposals is that someone with domain expertise must review outputs against verified source material before anything goes out the door; human-in-the-loop review is not optional. Kantiv is designed around this constraint: it surfaces verified content from your firm's own pursuit history as the retrieval layer, so the model is working from your actual project data rather than interpolating from a general corpus that has no knowledge of your clients, your wins, or your staff.
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